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Robot Interaction has always been a challenge in collaborative robotics. In tasks
comprising Inter-Robot Interaction, robot detection is very often needed. We
explore humanoid robots detection because, humanoid robots can be useful in many
scenarios, and everything from helping elderly people live in their own homes to
responding to disasters. Cameras are chosen because they are reach and cheap
sensors, and there are lots of mature two-dimensional (2D) and 3D computer
vision libraries which facilitate Image analysis. To tackle humanoid robot
detection effectively, we collected a data set of various humanoid robots with
different sizes in different environments. Afterward, we tested the well-known
cascade classifier in combination with several image descriptors like Histograms
of Oriented Gradients (HOG), Local Binary Patterns (LBP), etc. on this data set.
Among the feature sets, Haar-like has the highest accuracy, LBP the highest
recall, and HOG the highest precision. Considering Inter-Robot Interaction, it
is evident that false positives are less troublesome than false negatives, thus
LBP is more useful than the others.

This paper describes the motivation for the development of the HuroCup
competition and follows the rule development from its inaugural competition from
2002 to 2015. The history of HuroCup is broken down into its growing phase
(2002–2006), a time of explosive growth (2007–2011), and current
times. This paper describes the main research focus of HuroCup, the multi-event
humanoid robot competition: (a) active balancing, (b) complex motion planning,
and (c) human–robot interaction and shows how the various HuroCup events
relate to those research topics. This paper concludes with some medium- and
long-term goals of the rule development for HuroCup.

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